This paper proposes an Opinion Mining model, parameterized according to the reviewer profile. The work aims to highlight and resolve some issues resulting from previous activities in evaluating the goodness of the results obtained by the analysis of the reviews. A user profiling system provides the set of parameters to associate with the aspects and allows, working with the Opinion Mining system, to configure itself according to the user preferences.

An experimental analysis of a combination of Opinion Mining and Collaborative Filtering algorithms is presented. The analysis used the Yelp dataset in order to have both the textual reviews and the star ratings provided by the users. The Opinion Mining algorithm was used to work on the textual reviews, while the Collaborative Filtering worked on the star ratings. The research activity carried out shows that most of the Yelp users provided star ratings corresponding to the related textual review, but in many cases an inconsistence was evident. A set of thresholds and coefficients were applied in order to test a hypothesis about the influence of restaurant popularity on the user ratings. Interesting results have been obtained in terms of Root Mean Squared Error (RMSE).

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Online users are talking across social media sites, on public forums and within customer feedback channels about products, services and their experiences, as well as their likes and dislikes. The continuous monitoring of reviews is ever more important in order to identify leading topics and content categories and to understand how those topics and categories are relevant to customers according to their habits. In this context, the chapter proposes an Opinion Mining model to analyze and summarize reviews related to generic categories of products and services. The process, based on a linguistic approach to the analysis of the opinions expressed, includes the extraction of features terms from the reviews in generic domains. It is also capable to determine the positive or negative valence of the identified features exploiting FreeWordNet, a WordNet-based linguistic resource of adjectives and adverbs involved in the whole process.

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